A major U.S. based Automotive software provider is on a mission to improve customer retention, personalize recommendations and offers and improve overall operational and forecasting efficiency using a wide variety of data – internal, external from various dealerships, system providers, and third-party data, data enrichment partners and research data. Key to this strategy is to leverage an AI/ML based autonomous data ingestion, quality layer to streamline and automate the collection of data coming from various disparate systems. Especially a platform that enables smart curation and cleans automatically, improve overall data quality.
As a result, they were able to apply models to a mass of historical and transactional data coming from a range of sources with a more precise, effective approach to form customer segments, improve forecast efficiency, and even identify the impact of investments with the composition of marketing spend.
An automotive software provider found themselves dealing with a large amount of data coming from a wide variety of disparate sources that need to be ingested for reporting and analytics and set forth a journey to improve overall data quality. By this way, they shall address the challenges around dealing with constantly changing data quality issues, the inability to manage what you don’t measure, eliminate labor-intensive data quality checks, and get rid of manual cleansing. With a data quality-focused strategy, they hoped to create high-quality data for their reporting and analytics use.
Like most organizations, they faced a number of challenges: the growing amount of wide variety of data sources and disparate systems; changing needs of data quality rules, workflows; inability to consistently create a streamlined process; disorganized or inaccurate data; the inability to innovate and scale; manual process and workflow, — the list goes on. All making it impossible to automate a process around both subjective and objective dimensions of data quality and keep learning as the underlying data shifts.
Without knowing where to start and what can be done for their organization, it was challenging to bring data silos together especially when some are in cloud, on-premise and of different structure, form, and sizes. Further, the underlying data also had quality issues and normalization of data was a big challenge with each system have its own terminologies and taxonomies.
Implementing stand-alone tools were short-lived as the success was very limited and couldn’t be extended into a as there was no learning to make from an organizational data lifecycle. Further, it required silo efforts, and economies of scale weren’t leveraged. The firm knew they’d need a platform that learns data culture and adapts.
Increase in quality by autonomous smart curation
DQLabs focuses on data quality in all aspects of data management as its increasingly critical to the success of all strategic business imperatives. Selecting and applying the right data quality dimensions in the right business context leads to better business results, especially in operation efficiency, risk, innovation and competitiveness. DQLabs as it ingests data from silos auto senses the atomic and semantic type of the data to create a comprehensive data quality measurement using subjective and objective measures.
Further, DQLabs learns your organization-specific data culture through visual learning and reinforcement models. With this ability, it auto recommends and executes the necessary quantitative measurement at each attribute level and provides a data quality score – DQScore. This score enables organizations to measure at consensus and track as your data evolves.
We used DQLabs not only to measure data quality over time but also uses its three levels of curation – basic, reference, and advanced to help auto cleanse data as we ingest from various data sources. This Business-Driven Data Governance Approach focusing on Targeted Data Quality improvements provided an immediate impact on measuring how we are doing plus also improving the quality of the reports and analytical models we generate. The ingestion process involved merging a variety of data silos and enabling automated cleansing at all levels. For example, name attribute-related data quality challenges around missing spaces, double names, transliteration, nicknames, diacritics which took a lot of time to curate were now done in minutes.
DQLabs platform continuously improves data quality as an ongoing process and aligns with the organization’s overall Data and Analytics strategy to achieve the desired business outcomes. As new opportunities and challenges arise, governance decisions such as which data objects or business process to target for improvement, what “good” data quality looks like, and which metrics to introduce/retire are constantly evolved and measured. With an improved level of reporting and analytics, now more predictive analytics towards forecasting, customer segmentation, preferences, and recommendations helped to boost sales and overall customer experience.
PUBLIC, GOVERNMENT & NON-PROFIT
The City is one of the top-ranked metropolitan areas in the United States. The City’s regional economy is versatile and spread across various verticals, with a robust emphasis on life sciences, agribusiness education and research, logistics, manufacturing, aerospace, and professional services.
FINANCE & WEALTH MANAGEMENT
One of our customers is a leading U.S. based Asset Management service provider dealing with various businesses such as banks, brokerage firms, trust companies, insurance providers and credit unions which involves different data sources like the Investment Accounting System, Trading Systems